CN104243767A - Method for removing image noise - Google Patents

Method for removing image noise Download PDF

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Publication number
CN104243767A
CN104243767A CN201310233640.2A CN201310233640A CN104243767A CN 104243767 A CN104243767 A CN 104243767A CN 201310233640 A CN201310233640 A CN 201310233640A CN 104243767 A CN104243767 A CN 104243767A
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China
Prior art keywords
pixel
object pixel
value
core
noise
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CN201310233640.2A
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Chinese (zh)
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周宏隆
赵善隆
林慧珊
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Glomerocryst Semiconductor Ltd Co
Altek Corp
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Glomerocryst Semiconductor Ltd Co
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  • Facsimile Image Signal Circuits (AREA)
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Abstract

The invention provides a method for removing image noise. The method includes the following steps: receiving an image to be processed, wherein the image to be processed comprises a plurality of pixels to be processed; selecting one of the pixels to be processed for serving as a target pixel, and conducting texture analysis on the target pixel to judge whether the target pixel is located in a shadow area or not; when the target pixel is not located in the shadow area, executing trilateral noise removing on the target pixel to generate the processed pixel. In this way, according to the method for removing the image noise, the image quality can be improved by removing the image noise.

Description

Remove the method for picture noise
Technical field
The invention relates to a kind of image processing techniques, and relate to a kind of method removing picture noise especially.
Background technology
In the middle of image procossing, know and present edge and strengthen edge, remove unnecessary noise is the most basic work simultaneously.Digital image is often because the electronic component of camera own or external factor produce noise as the impact such as temperature, light in the process of shooting, and in order in response to these noise problems, the method for many filtering noises is also suggested successively.But the greatest problem of noise filtering to reduce the sharpness of image.In other words, noise filtering also may cause blurred picture (blurred Image) situation.In general, such situation can apply edge enhancement algorithm to improve the sharpness of image.But the signal of noise also usually can strengthen by the image procossing doing edge enhancing simultaneously.
In the method for existing removal picture noise, the practice of some uses the methods such as median filter (Median filter), average filter (Mean filter) or low pass filter (Low Pass Filter, LPF) to be removed by picture noise.Above-mentioned several method uses the pixel in whole image to be averaged computing to remove picture noise, do not consider the smooth region in image and details area problem.The existing another kind of practice uses two-sided filter (Bilateral filter), adjusts by adjustment weighted value the intensity removing noise.But, if weighted value is too high, then may cause blurred picture problem; If weighted value is too low, then may reduce the ability removing noise, therefore how to make trade-offs between removal noise and reservation image detail information, be an important topic in fact.In addition, in the middle of strong noise image, the simple limited efficiency using two-sided filter to promote image quality.
Summary of the invention
The invention provides a kind of method removing picture noise, in order to the noise in filtering image effectively, and can also can effectively retain image detail and marginal information while removal picture noise.
The method of removal picture noise of the present invention, comprises the following steps: first to receive pending image, and wherein pending image comprises most pending pixels; One of them choosing pending pixel as object pixel, and carries out texture analysis (texture analysis) to this object pixel, to judge whether this object pixel is positioned at shadow region (shading area); When this object pixel is not positioned at shadow region, then three limit noise removes (trilateral noise reduction) process is performed to this object pixel, to produce the rear pixel of process.
In one embodiment of this invention, the method of above-mentioned removal picture noise also comprises when this object pixel is positioned at shadow region, then double sided noise is performed to this object pixel and remove (Bilateral noise reduction) process, to produce the rear pixel of process.
In one embodiment of this invention, above-mentionedly three limits are performed to this object pixel reduce the step of noise processed and comprise: first selected the first core (Kernel) centered by this object pixel respectively, and selected the second core centered by each reference pixel.Then, each pixel in the first core and each respective pixel in the second core are calculated, to obtain the Similarity value that each reference pixel corresponds to this object pixel.Each reference pixel determines corresponding pixel weight value according to each Similarity value again.Finally, the pixel value of each reference pixel belonged in the shade (mask) of this object pixel is multiplied by corresponding pixel weight value, to obtain the rear pixel of process.
In one embodiment of this invention, above-mentionedly to calculate each pixel in the first core and each respective pixel in the second core, the formula corresponding to the Similarity value of this object pixel to obtain each reference pixel is: Sim j = w c Σ t = 1 M Σ s = 1 M ( w l d · | k l i - k l j | ) , l = s + t × M . Wherein, Sim jfor the Similarity value of reference pixel, w cfor confidence weighted value, for distance weighting value, for the first core centered by object pixel, for the second core centered by reference pixel.
In one embodiment of this invention, above-mentioned confidence weighted value w cdecided by the distance between reference pixel and object pixel.
In one embodiment of this invention, the above-mentioned step to this object pixel execution double sided noise Transformatin comprises: first selected shade centered by this object pixel.Then, calculating belongs to each reference pixel in this shade corresponding to the distance weighting value of this object pixel and close to intensity (intensity closeness) weighted value.Further, each reference pixel carries out computing according to each distance weighting value and each close to intensity weighted value, to obtain the rear pixel of process.
In one embodiment of this invention, each above-mentioned reference pixel according to each distance weighting value and each formula carrying out computing close to intensity weighted value is: P i = Σ t = 1 N Σ s = 1 N w j d · w j r · P j , j = s + t × N . Wherein, P ifor object pixel, P jfor reference pixel, for the distance weighting value of reference pixel, for reference pixel close to intensity weighted value.
In one embodiment of this invention, the block size of first and second above-mentioned core is M × M, and the block size of this shade is N × N, wherein M<N and M, N are all the positive integer being greater than 0.
Based on above-mentioned, the present invention proposes a kind of integrated process structure and removes picture noise, to promote the quality of image.Wherein, by carrying out texture analysis to image, adaptability choice for use double sided noise is removed or three limit noise remove algorithms, while reaching removal picture noise, also remain image detail information.
For above-mentioned feature and advantage of the present invention can be become apparent, special embodiment below, and coordinate accompanying drawing to be described in detail below.
Accompanying drawing explanation
Fig. 1 is according to a kind of flow chart removing the method for picture noise shown by one embodiment of the invention;
Fig. 2 is the flow chart according to the double sided noise Transformatin method that performs object pixel shown by one embodiment of the invention;
Fig. 3 is according to the shade schematic diagram shown by one embodiment of the invention;
Fig. 4 is according to flow chart object pixel execution (improvement) three limit being reduced to method for processing noise shown by one embodiment of the invention;
Fig. 5 is the rough schematic according to the shade shown by another embodiment of the present invention and core thereof.
Description of reference numerals:
300,500: shade;
Pi: object pixel;
Pj: reference pixel;
K 1: the first core;
K 2: the second core;
S110 ~ S150: each step removing the method for picture noise;
S210 ~ S230: each step performing double sided noise Transformatin method;
S410 ~ S440: perform each step that three limits reduce method for processing noise.
Embodiment
The present invention proposes a kind of integrated process structure and removes picture noise problem.It uses double sided noise to remove algorithm adaptively according to image texture information or uses non local average (Non-local mean) noise remove algorithm of improvement, and the advantage of two kinds of algorithms can be retained simultaneously, according to this while removal picture noise, reach the effect retaining image detail information.In order to make content of the present invention more clear, below enumerate the example that embodiment can be implemented really according to this as the present invention.The embodiment proposed only as explaining orally use, is not used for limiting interest field of the present invention.
Fig. 1 is according to a kind of flow chart removing the method for picture noise shown by one embodiment of the invention.Please refer to Fig. 1, the method flow of the present embodiment is suitable for being used in digital camera, digital single anti-(Digital Single Lens Reflex, DSLR) camera, digital code camera (Digital Video Camcorder, the image acquiring device such as DVC), or other have the electronic installation such as smart mobile phone, panel computer of image processing function, are not limited to above-mentioned.
First, in step s 110, first receive pending image, wherein pending image comprises most pending pixels.Then, in step S120, choose one of them pixel in pending pixel as object pixel, and texture analysis (texture analysis) process is carried out to this object pixel.Wherein, texture analysis process described herein can select existing texture analysis algorithm to be applied by those skilled in the art, therefore is not limited at this.
Next, in step S130, according to the result of above-mentioned texture analysis, judge whether this object pixel is positioned at shadow region (shading area).Shadow region described herein representative image has less profile (contour) or marginal existence in the region, therefore, can be referred to as smooth region again.In other words, if object pixel is positioned at shadow region, shadows pixels (shading pixel) also can be referred to as.
In step S140, when this object pixel is positioned at shadow region, then two-sided filter (Bilateral filter) can be utilized to perform double sided noise Transformatin to this object pixel, to produce the rear pixel of process.On the contrary, in step S150, when this object pixel is not positioned at shadow region, the non local average filter of improvement (improved Non-Local means filter) then can be utilized to perform three limit noise removes (trilateral noise reduction) process to this object pixel, to produce the rear pixel of process.
Accordingly, the method that the present embodiment provides uses two kinds of different filters adaptively based on the result after texture analysis, and while removal picture noise, can reach the effect retaining image detail information.
Below (improvement) performed by the non local average filter then removing algorithm and improvement for the double sided noise performed by two-sided filter three limit noise remove algorithm be described in detail.
Need first illustrate, the filter removing picture noise is broadly divided into two classes, and one is local average filter, and another is non local average filter.Wherein, it is a kind of common and effective local average filter that double sided noise performed by two-sided filter removes algorithm.But the noise problem that no matter to be non-local average filter or bidirectional filter be all uses the characteristic of Gaussian filter (Gaussian filter) to come in removal of images.
Fig. 2 is the flow chart according to the double sided noise Transformatin method that performs object pixel shown by one embodiment of the invention.Wherein, Fig. 2 is a kind of detailed embodiment of the step S140 of Fig. 1.
Please refer to Fig. 2, in step S210, first selected shade centered by this object pixel.In the present embodiment, the block size of shade to be N × N, N be greater than 0 positive integer.That is, the computing selected two-dimension square image pixel array to carry out and remove noise can be enclosed in this step centered by object pixel.For example, Fig. 3 is according to the shade schematic diagram shown by one embodiment of the invention.Please refer to Fig. 3, shade 300 is such as 5 × 5(N=5) array, it comprises an an object pixel Pi and 24 reference pixel Pj.
Then, in step S220, calculate and belong to each reference pixel in this shade corresponding to the distance weighting value of this object pixel and close to intensity (intensity closeness) weighted value.In detail, each reference pixel carries out the formula of computing as shown in the formula shown in (1) according to each distance weighting value and each close to intensity weighted value:
P i = &Sigma; t = 1 N &Sigma; s = 1 N w j d &CenterDot; w j r &CenterDot; P j , j = s + t &times; N - - - ( 1 )
Wherein, P ifor object pixel, P jfor reference pixel, for the distance weighting value of reference pixel, for reference pixel close to intensity weighted value.
Bidirectional filter use with apart from and each pixel of rebuilding close to two weighted values that intensity (similarity) is relevant in pending image.Therefore, the reference value that distance weighting value refers to the nearer reference pixel Pj of distance objective pixel Pi is higher, makes when according to each reference pixel reconstructed object pixel Pi, the closer to the distance weighting value of the reference pixel Pj of object pixel Pi higher.Relevant with similarity refers in each reference pixel Pj around object pixel Pi close to intensity weighted value, the reference pixel Pj more similar to object pixel Pi has higher reference value, make when according to each reference pixel Pj reconstructed object pixel Pi, the Pj of the reference pixel more similar to object pixel Pi close to intensity weighted value higher.
Each reference pixel distance weighting value with all to obtain close to intensity weighted value after, just can subsequent steps S230, each reference pixel carries out computing according to each distance weighting value and each close to intensity weighted value, to obtain the rear pixel (object pixel namely after calculation process) of process.
Fig. 4 is according to flow chart object pixel execution (improvement) three limit being reduced to method for processing noise shown by one embodiment of the invention.Wherein, Fig. 4 is a kind of detailed embodiment of the step S150 of Fig. 1.
Please refer to Fig. 4, in step S410, first select the first core (Kernel) centered by object pixel respectively, and selected the second core centered by each reference pixel.Being different from bidirectional filter is will determine its weighted value respectively for each reference pixel, and it is in order to check centered by object pixel, to be centered around the similarity of the reduced size shade around object pixel that three limits reduce method for processing noise.In the present embodiment reduced size shade is referred to as " core ".In the present embodiment, the block size of shade is N × N, and the block size of first and second core is N × N, wherein M<N and M, N are all the positive integer being greater than 0.
Then, in step S420, each pixel in the first core and each respective pixel in the second core are calculated, to obtain the Similarity value that each reference pixel corresponds to this object pixel.For example, Fig. 5 is the rough schematic according to the shade shown by another embodiment of the present invention and core thereof.Please refer to Fig. 5, shade 500 is centered by object pixel Pi, around object pixel Pi around block be such as the first core K 1, around reference pixel Pj around block be such as the second core K 2.
Wherein, each pixel in the first core and each respective pixel in the second core are calculated, correspond to the formula of the Similarity value of this object pixel as shown in the formula shown in (2) to obtain each reference pixel:
Sim j = w c &Sigma; t = 1 M &Sigma; s = 1 M ( w l d &CenterDot; | K l i - K l j | ) , l = s + t &times; M - - - ( 2 )
Wherein, Sim jfor the Similarity value of reference pixel Pj, w cfor confidence weighted value, for distance weighting value, for the first core centered by object pixel Pi, for the second core centered by reference pixel Pj.
Similarity value Sim jdo similarity by two cores to measure gained.If Similarity value Sim jhigher, represent similarity lower.On the contrary, if Similarity value Sim jlower, represent similarity higher.Specifically the present invention, in the middle of the formula (2) calculating Similarity value, utilizes confidence weighted value w cadjust the intensity removing noise.Wherein, confidence weighted value w cby reference pixel P jwith object pixel P ibetween distance decide.
In step S430, each reference pixel determines corresponding pixel weight value according to each Similarity value again.Finally, in step S440, the pixel value of each reference pixel belonged in the shade of this object pixel is multiplied by corresponding pixel weight value, to obtain the rear pixel of process.Accordingly, the present invention calculates the method for Similarity value by improvement, reduces method for processing noise more can promote image quality compared to existing non local average algorithm to make three limits.
In sum, the present invention removes the method for picture noise, it uses double sided noise to remove algorithm adaptively according to image texture information or uses three limits to reduce method for processing noise, and the advantage of two kinds of algorithms can be retained simultaneously, according to this while removal picture noise, reach the effect retaining image detail information, and the problem of blurred picture can not be produced.In addition, the present invention calculates the method for Similarity value by improvement, reduces method for processing noise more can promote image quality compared to existing non local average algorithm to make three limits.The image acquiring device adopting the present invention to remove picture noise method effectively can be lifted at image output quality during ISO.
Last it is noted that above each embodiment is only in order to illustrate technical scheme of the present invention, be not intended to limit; Although with reference to foregoing embodiments to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein some or all of technical characteristic; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the scope of various embodiments of the present invention technical scheme.

Claims (8)

1. remove a method for picture noise, it is characterized in that, comprising:
Receive a pending image, wherein this pending image comprises most pending pixels;
One of them choosing those pending pixels as an object pixel, and carries out a texture analysis to this object pixel, to judge whether this object pixel is positioned at a shadow region; And
When this object pixel is not positioned at this shadow region, one or three limit noise removal process are performed to this object pixel, to produce pixel after a process.
2. the method for removal picture noise according to claim 1, is characterized in that, also comprise:
When this object pixel is positioned at this shadow region, then a bilateral noise removal process is performed to this object pixel, to produce pixel after a process.
3. the method for removal picture noise according to claim 1, is characterized in that, the step this object pixel being performed to this three limit reduction noise processed comprises:
Selected one first core centered by this object pixel respectively, and selected one second core centered by each this reference pixel;
Each respective pixel in each pixel in this first core and this second core is calculated, corresponds to a Similarity value of this object pixel to obtain respectively this reference pixel;
Respectively this reference pixel foundation pixel weight value that respectively decision of this Similarity value is corresponding; And
The pixel value of respectively this reference pixel in a shade of this object pixel is multiplied by this corresponding pixel weight value, to obtain pixel after this process.
4. the method for removal picture noise according to claim 1, it is characterized in that, respectively this pixel in this first core and respectively this respective pixel in this second core are calculated, correspond to the formula of this Similarity value of this object pixel as shown in the formula (1) to obtain respectively this reference pixel:
Sim j = w c &Sigma; t = 1 M &Sigma; s = 1 M ( w l d &CenterDot; | K l i - K l i | ) , l = s + t &times; M - - - ( 1 )
Wherein, Sim jfor this Similarity value of reference pixel j, w cfor confidence weighted value, for distance weighting value, for this first core centered by this object pixel i, for this second core centered by this reference pixel j.
5. the method for removal picture noise according to claim 4, is characterized in that, confidence weighted value w cdecided by the distance between this reference pixel j and this object pixel i.
6. the method for removal picture noise according to claim 2, is characterized in that, the step this object pixel being performed to this double sided noise Transformatin comprises:
A selected shade centered by this object pixel;
Each reference pixel calculated in this shade corresponds to a distance weighting value and of this object pixel close to intensity weighted value; And
Respectively this reference pixel is according to each this distance weighting value and respectively this carries out computing close to intensity weighted value, to obtain pixel after this process.
7. the method for removal picture noise according to claim 6, is characterized in that, respectively this reference pixel according to each this distance weighting value and respectively this formula carrying out computing close to intensity weighted value as shown in the formula (2):
P i = &Sigma; t = 1 N &Sigma; s = 1 N w j d &CenterDot; w j r &CenterDot; P j , j = s + t &times; N - - - ( 2 )
Wherein, P ifor this object pixel i, P jfor this reference pixel j, for this distance weighting value of this reference pixel j, for this of this reference pixel j is close to intensity weighted value.
8. the method for removal picture noise according to claim 3, is characterized in that, this first is M × M with the block size of this second core, and the block size of this shade is N × N, wherein M<N and M, N are all the positive integer being greater than 0.
CN201310233640.2A 2013-06-13 2013-06-13 Method for removing image noise Pending CN104243767A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109391783A (en) * 2017-08-14 2019-02-26 佳能株式会社 Image processing apparatus, image processing method and storage medium
CN114615448A (en) * 2020-12-09 2022-06-10 爱思开海力士有限公司 Image sensing device

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
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王世秀；罗晓军: "一种去除高斯噪音的新方法", 《科学技术与工程》 *
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109391783A (en) * 2017-08-14 2019-02-26 佳能株式会社 Image processing apparatus, image processing method and storage medium
CN114615448A (en) * 2020-12-09 2022-06-10 爱思开海力士有限公司 Image sensing device

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Application publication date: 20141224